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AUV adaptive sampling methods: a review


Hwang, J and Bose, N and Fan, S, AUV adaptive sampling methods: a review, Applied Sciences, 9, (15) Article 3145. ISSN 2076-3417 (2019) [Refereed Article]


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Copyright 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license

DOI: doi:10.3390/app9153145


Autonomous underwater vehicles (AUVs) are unmanned marine robots that have been used for a broad range of oceanographic missions. They are programmed to perform at various levels of autonomy, including autonomous behaviours and intelligent behaviours. Adaptive sampling is one class of intelligent behaviour that allows the vehicle to autonomously make decisions during a mission in response to environment changes and vehicle state changes. Having a closed-loop control architecture, an AUV can perceive the environment, interpret the data and take follow-up measures. Thus, the mission plan can be modified, sampling criteria can be adjusted, and target features can be traced. This paper presents an overview of existing adaptive sampling techniques. Included are adaptive mission uses and underlying methods for perception, interpretation and reaction to underwater phenomena in AUV operations. The potential for future research in adaptive missions is discussed.

Item Details

Item Type:Refereed Article
Keywords:autonomous underwater vehicle (AUV), adaptive sampling, oil spill delineation, maritime robotics, underwater feature tracking, in-situ sensors, sensor fusion
Research Division:Engineering
Research Group:Maritime engineering
Research Field:Special vehicles
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence, surveillance and space
UTAS Author:Hwang, J (Miss Jimin Hwang)
UTAS Author:Bose, N (Professor Neil Bose)
ID Code:142001
Year Published:2019
Web of Science® Times Cited:23
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2020-12-08
Last Modified:2022-08-29
Downloads:13 View Download Statistics

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